Urban Vertical Greening Optimization Supported by Deep Learning and Remote Sensing Technology and Its Application in Smart Ecological Cities

Abstract

This research systematically investigates urban three-dimensional greening layout optimization and smart eco-city construction using deep learning and remote sensing technology. An improved U-Net++ architecture combined with multi-source remote sensing data achieved high-precision recognition of urban three-dimensional greening with 92.8% overall accuracy. Analysis of spatiotemporal evolution patterns in Shanghai, Hangzhou, and Nanjing revealed that three-dimensional greening shows a development trend from demonstration to popularization, with 16.5% annual growth rate.The study quantitatively assessed ecological benefits of various three-dimensional greening types. Results indicate that modular vertical greening and intensive roof gardens yield highest ecological benefits, while climbing-type vertical greening and extensive roof gardens offer optimal benefit-cost ratios. Integration of multiple forms generates 15–22% synergistic enhancement.Compared with traditional planning, the multi-objective optimization-based layout achieved 27.5% increase in carbon sequestration, 32.6% improvement in temperature regulation, 35.8% enhancement in stormwater management, and 42.3% rise in biodiversity index. Three pilot projects validated that actual ecological benefits reached 90.3–102.3% of predicted values.Multi-scenario simulations indicate optimized layouts can reduce urban heat island intensity by 15.2–18.7%, increase carbon neutrality contribution to 8.6–10.2%, and decrease stormwater runoff peaks by 25.3–32.6%. The findings provide technical methods for urban three-dimensional greening optimization and smart eco-city construction, promoting sustainable urban development.